update
This commit is contained in:
@@ -7,6 +7,7 @@ const PROJECT_CONTAINERS = [
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'paddleocr-vl-cpu-test',
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'paddleocr-vl-full-test',
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'minicpm-test',
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'nanonets-test',
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];
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// Image configurations
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@@ -74,6 +75,19 @@ export const IMAGES = {
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healthEndpoint: 'http://localhost:8000/health',
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healthTimeout: 600000, // 10 minutes for model loading (vLLM + PP-DocLayoutV2)
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} as IImageConfig,
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// Nanonets-OCR-s - Document OCR optimized VLM (Qwen2.5-VL-3B fine-tuned)
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nanonetsOcr: {
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name: 'nanonets-ocr',
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dockerfile: 'Dockerfile_nanonets_ocr',
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buildContext: '.',
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containerName: 'nanonets-test',
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ports: ['8000:8000'],
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volumes: ['ht-huggingface-cache:/root/.cache/huggingface'],
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gpus: true,
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healthEndpoint: 'http://localhost:8000/health',
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healthTimeout: 300000, // 5 minutes for model loading
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} as IImageConfig,
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};
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/**
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@@ -383,3 +397,14 @@ export async function ensureQwen3Vl(): Promise<boolean> {
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// Then ensure Qwen3-VL 8B is pulled
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return ensureOllamaModel('qwen3-vl:8b');
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}
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/**
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* Ensure Nanonets-OCR-s service is running (via vLLM)
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* Document OCR optimized VLM based on Qwen2.5-VL-3B
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*/
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export async function ensureNanonetsOcr(): Promise<boolean> {
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if (!isGpuAvailable()) {
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console.log('[Docker] WARNING: Nanonets-OCR-s requires GPU, but none detected');
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}
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return ensureService(IMAGES.nanonetsOcr);
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}
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530
test/test.bankstatements.nanonets.ts
Normal file
530
test/test.bankstatements.nanonets.ts
Normal file
@@ -0,0 +1,530 @@
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/**
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* Bank statement extraction using Nanonets-OCR-s + Qwen3 (two-stage pipeline)
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*
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* Stage 1: Nanonets-OCR-s converts document pages to markdown (its strength)
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* Stage 2: Qwen3 extracts structured JSON from the combined markdown
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*
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* This leverages each model's strengths:
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* - Nanonets: Document OCR with semantic tags
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* - Qwen3: Text understanding and JSON extraction
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*/
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import { tap, expect } from '@git.zone/tstest/tapbundle';
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import * as fs from 'fs';
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import * as path from 'path';
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import { execSync } from 'child_process';
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import * as os from 'os';
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import { ensureNanonetsOcr, ensureMiniCpm } from './helpers/docker.js';
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const NANONETS_URL = 'http://localhost:8000/v1';
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const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
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const OLLAMA_URL = 'http://localhost:11434';
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const QWEN_MODEL = 'qwen3:8b';
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interface ITransaction {
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date: string;
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counterparty: string;
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amount: number;
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}
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// Nanonets-specific prompt for document OCR to markdown
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const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
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Return the tables in html format.
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Return the equations in LaTeX representation.
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If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
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Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
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Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
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// JSON extraction prompt for Qwen3
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const JSON_EXTRACTION_PROMPT = `You are a financial data extractor. Below is a bank statement converted to text/markdown. Extract ALL transactions from it as a JSON array.
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IMPORTANT RULES:
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1. Each transaction has: date, description/counterparty, and an amount
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2. Amount is NEGATIVE for money going OUT (debits, payments, withdrawals)
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3. Amount is POSITIVE for money coming IN (credits, deposits, refunds)
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4. Date format: YYYY-MM-DD
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5. Do NOT include: opening balance, closing balance, subtotals, headers, or summary rows
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6. Only include actual transactions with a specific date and amount
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Return ONLY this JSON format, no explanation:
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[
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{"date": "2021-06-01", "counterparty": "COMPANY NAME", "amount": -25.99},
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{"date": "2021-06-02", "counterparty": "DEPOSIT FROM", "amount": 100.00}
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]
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BANK STATEMENT TEXT:
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`;
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/**
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* Convert PDF to PNG images using ImageMagick
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*/
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function convertPdfToImages(pdfPath: string): string[] {
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const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
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const outputPattern = path.join(tempDir, 'page-%d.png');
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try {
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// Use 150 DPI to keep images within model's context length
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execSync(
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`convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
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{ stdio: 'pipe' }
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);
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const files = fs.readdirSync(tempDir).filter((f: string) => f.endsWith('.png')).sort();
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const images: string[] = [];
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for (const file of files) {
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const imagePath = path.join(tempDir, file);
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const imageData = fs.readFileSync(imagePath);
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images.push(imageData.toString('base64'));
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}
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return images;
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} finally {
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fs.rmSync(tempDir, { recursive: true, force: true });
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}
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}
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/**
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* Stage 1: Convert a single page to markdown using Nanonets-OCR-s
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*/
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async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
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console.log(` [Nanonets] Converting page ${pageNum} to markdown...`);
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const startTime = Date.now();
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const response = await fetch(`${NANONETS_URL}/chat/completions`, {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': 'Bearer dummy',
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},
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body: JSON.stringify({
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model: NANONETS_MODEL,
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messages: [{
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role: 'user',
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content: [
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{ type: 'image_url', image_url: { url: `data:image/png;base64,${image}` }},
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{ type: 'text', text: NANONETS_OCR_PROMPT },
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],
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}],
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max_tokens: 4096,
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temperature: 0.0,
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}),
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});
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const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
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if (!response.ok) {
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const errorText = await response.text();
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console.log(` [Nanonets] ERROR page ${pageNum}: ${response.status} - ${errorText}`);
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throw new Error(`Nanonets API error: ${response.status}`);
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}
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const data = await response.json();
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const content = (data.choices?.[0]?.message?.content || '').trim();
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console.log(` [Nanonets] Page ${pageNum} converted (${elapsed}s, ${content.length} chars)`);
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return content;
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}
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/**
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* Stage 1: Convert all pages to markdown using Nanonets-OCR-s
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*/
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async function convertDocumentToMarkdown(images: string[]): Promise<string> {
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console.log(` [Stage 1] Converting ${images.length} page(s) to markdown with Nanonets-OCR-s...`);
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const markdownPages: string[] = [];
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for (let i = 0; i < images.length; i++) {
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const markdown = await convertPageToMarkdown(images[i], i + 1);
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markdownPages.push(`--- PAGE ${i + 1} ---\n${markdown}`);
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}
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const fullMarkdown = markdownPages.join('\n\n');
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console.log(` [Stage 1] Complete: ${fullMarkdown.length} chars total`);
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return fullMarkdown;
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}
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/**
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* Ensure Qwen3 model is available
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*/
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async function ensureQwen3(): Promise<boolean> {
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try {
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const response = await fetch(`${OLLAMA_URL}/api/tags`);
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if (response.ok) {
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const data = await response.json();
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const models = data.models || [];
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if (models.some((m: { name: string }) => m.name === QWEN_MODEL)) {
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console.log(` [Ollama] Model available: ${QWEN_MODEL}`);
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return true;
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}
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}
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} catch {
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return false;
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}
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console.log(` [Ollama] Pulling ${QWEN_MODEL}...`);
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const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ name: QWEN_MODEL, stream: false }),
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});
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return pullResponse.ok;
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}
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/**
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* Stage 2: Extract transactions from markdown using Qwen3
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*/
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async function extractTransactionsFromMarkdown(markdown: string, queryId: string): Promise<ITransaction[]> {
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console.log(` [${queryId}] Sending markdown to ${QWEN_MODEL}...`);
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const startTime = Date.now();
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const response = await fetch(`${OLLAMA_URL}/api/chat`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model: QWEN_MODEL,
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messages: [{
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role: 'user',
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content: JSON_EXTRACTION_PROMPT + markdown,
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}],
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stream: false,
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options: {
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num_predict: 8000,
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temperature: 0.1,
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},
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}),
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});
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const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
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if (!response.ok) {
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console.log(` [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
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throw new Error(`Ollama API error: ${response.status}`);
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}
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const data = await response.json();
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const content = (data.message?.content || '').trim();
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console.log(` [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
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return parseJsonResponse(content, queryId);
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}
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/**
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* Sanitize JSON string - fix common issues from LLM output
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*/
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function sanitizeJson(jsonStr: string): string {
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let s = jsonStr;
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// Fix +number (e.g., +93.80 -> 93.80) - JSON doesn't allow + prefix
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s = s.replace(/"amount"\s*:\s*\+/g, '"amount": ');
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s = s.replace(/:\s*\+(\d)/g, ': $1');
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||||
// Fix European number format with thousands separator
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||||
s = s.replace(/"amount"\s*:\s*(-?)(\d{1,3})\.(\d{3})\.(\d{2})\b/g, '"amount": $1$2$3.$4');
|
||||
|
||||
// Fix trailing commas before ] or }
|
||||
s = s.replace(/,\s*([}\]])/g, '$1');
|
||||
|
||||
// Fix unescaped newlines/tabs inside strings
|
||||
s = s.replace(/"([^"\\]*)\n([^"]*)"/g, '"$1 $2"');
|
||||
s = s.replace(/"([^"\\]*)\t([^"]*)"/g, '"$1 $2"');
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||||
|
||||
// Remove control characters
|
||||
s = s.replace(/[\x00-\x08\x0B\x0C\x0E-\x1F]/g, ' ');
|
||||
|
||||
return s;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse amount from various formats
|
||||
*/
|
||||
function parseAmount(value: unknown): number {
|
||||
if (typeof value === 'number') return value;
|
||||
if (typeof value !== 'string') return 0;
|
||||
|
||||
let s = value.replace(/[€$£\s]/g, '').replace('−', '-').replace('–', '-');
|
||||
// European format: comma is decimal
|
||||
if (s.includes(',') && s.indexOf(',') > s.lastIndexOf('.')) {
|
||||
s = s.replace(/\./g, '').replace(',', '.');
|
||||
} else {
|
||||
s = s.replace(/,/g, '');
|
||||
}
|
||||
return parseFloat(s) || 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse JSON response into transactions
|
||||
*/
|
||||
function parseJsonResponse(response: string, queryId: string): ITransaction[] {
|
||||
console.log(` [${queryId}] Parsing response...`);
|
||||
|
||||
// Remove thinking tags if present (Qwen3 may include <think>...</think>)
|
||||
let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
|
||||
|
||||
// Try to find JSON in markdown code block
|
||||
const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
let jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
|
||||
|
||||
// Sanitize JSON
|
||||
jsonStr = sanitizeJson(jsonStr);
|
||||
|
||||
try {
|
||||
const parsed = JSON.parse(jsonStr);
|
||||
if (Array.isArray(parsed)) {
|
||||
const txs = parsed.map(tx => ({
|
||||
date: String(tx.date || ''),
|
||||
counterparty: String(tx.counterparty || tx.description || ''),
|
||||
amount: parseAmount(tx.amount),
|
||||
}));
|
||||
console.log(` [${queryId}] Parsed ${txs.length} transactions`);
|
||||
return txs;
|
||||
}
|
||||
} catch (e) {
|
||||
console.log(` [${queryId}] Direct parse failed: ${(e as Error).message}`);
|
||||
|
||||
// Try to find JSON array pattern
|
||||
const arrayMatch = jsonStr.match(/\[[\s\S]*\]/);
|
||||
if (arrayMatch) {
|
||||
try {
|
||||
const parsed = JSON.parse(sanitizeJson(arrayMatch[0]));
|
||||
if (Array.isArray(parsed)) {
|
||||
const txs = parsed.map(tx => ({
|
||||
date: String(tx.date || ''),
|
||||
counterparty: String(tx.counterparty || tx.description || ''),
|
||||
amount: parseAmount(tx.amount),
|
||||
}));
|
||||
console.log(` [${queryId}] Parsed ${txs.length} transactions (array match)`);
|
||||
return txs;
|
||||
}
|
||||
} catch (e2) {
|
||||
console.log(` [${queryId}] Array parse failed: ${(e2 as Error).message}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
console.log(` [${queryId}] PARSE FAILED - returning empty array`);
|
||||
return [];
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare two transaction arrays for consensus
|
||||
*/
|
||||
function transactionArraysMatch(a: ITransaction[], b: ITransaction[]): boolean {
|
||||
if (a.length !== b.length) return false;
|
||||
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
const dateMatch = a[i].date === b[i].date;
|
||||
const amountMatch = Math.abs(a[i].amount - b[i].amount) < 0.01;
|
||||
if (!dateMatch || !amountMatch) return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 2: Extract transactions using Qwen3 with consensus
|
||||
*/
|
||||
async function extractWithConsensus(markdown: string): Promise<ITransaction[]> {
|
||||
const MAX_ATTEMPTS = 3;
|
||||
console.log(` [Stage 2] Extracting transactions with ${QWEN_MODEL} (consensus)...`);
|
||||
|
||||
for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
|
||||
console.log(`\n [Stage 2] --- Attempt ${attempt}/${MAX_ATTEMPTS} ---`);
|
||||
|
||||
// Extract twice in parallel
|
||||
const [txs1, txs2] = await Promise.all([
|
||||
extractTransactionsFromMarkdown(markdown, `A${attempt}Q1`),
|
||||
extractTransactionsFromMarkdown(markdown, `A${attempt}Q2`),
|
||||
]);
|
||||
|
||||
console.log(` [Stage 2] Results: Q1=${txs1.length} txs, Q2=${txs2.length} txs`);
|
||||
|
||||
if (txs1.length > 0 && transactionArraysMatch(txs1, txs2)) {
|
||||
console.log(` [Stage 2] CONSENSUS REACHED: ${txs1.length} transactions`);
|
||||
return txs1;
|
||||
}
|
||||
|
||||
console.log(` [Stage 2] NO CONSENSUS`);
|
||||
}
|
||||
|
||||
// Fallback: use last response
|
||||
console.log(`\n [Stage 2] === FALLBACK ===`);
|
||||
const fallback = await extractTransactionsFromMarkdown(markdown, 'FALLBACK');
|
||||
console.log(` [Stage 2] ~ FALLBACK RESULT: ${fallback.length} transactions`);
|
||||
return fallback;
|
||||
}
|
||||
|
||||
/**
|
||||
* Full pipeline: PDF -> Images -> Markdown -> JSON
|
||||
*/
|
||||
async function extractTransactions(images: string[]): Promise<ITransaction[]> {
|
||||
// Stage 1: Convert to markdown
|
||||
const markdown = await convertDocumentToMarkdown(images);
|
||||
|
||||
// Stage 2: Extract transactions with consensus
|
||||
const transactions = await extractWithConsensus(markdown);
|
||||
|
||||
// Log all transactions
|
||||
console.log(`\n [Result] Extracted ${transactions.length} transactions:`);
|
||||
for (let i = 0; i < transactions.length; i++) {
|
||||
const tx = transactions[i];
|
||||
console.log(` ${(i + 1).toString().padStart(2)}. ${tx.date} | ${tx.counterparty.substring(0, 30).padEnd(30)} | ${tx.amount >= 0 ? '+' : ''}${tx.amount.toFixed(2)}`);
|
||||
}
|
||||
|
||||
return transactions;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare extracted transactions against expected
|
||||
*/
|
||||
function compareTransactions(
|
||||
extracted: ITransaction[],
|
||||
expected: ITransaction[]
|
||||
): { matches: number; total: number; errors: string[]; variations: string[] } {
|
||||
const errors: string[] = [];
|
||||
const variations: string[] = [];
|
||||
let matches = 0;
|
||||
|
||||
for (let i = 0; i < expected.length; i++) {
|
||||
const exp = expected[i];
|
||||
const ext = extracted[i];
|
||||
|
||||
if (!ext) {
|
||||
errors.push(`Missing transaction ${i}: ${exp.date} ${exp.counterparty}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
const dateMatch = ext.date === exp.date;
|
||||
const amountMatch = Math.abs(ext.amount - exp.amount) < 0.01;
|
||||
|
||||
if (dateMatch && amountMatch) {
|
||||
matches++;
|
||||
if (ext.counterparty !== exp.counterparty) {
|
||||
variations.push(`[${i}] "${exp.counterparty}" -> "${ext.counterparty}"`);
|
||||
}
|
||||
} else {
|
||||
errors.push(`Mismatch at ${i}: expected ${exp.date}/${exp.amount}, got ${ext.date}/${ext.amount}`);
|
||||
}
|
||||
}
|
||||
|
||||
if (extracted.length > expected.length) {
|
||||
errors.push(`Extra transactions: ${extracted.length - expected.length}`);
|
||||
}
|
||||
|
||||
return { matches, total: expected.length, errors, variations };
|
||||
}
|
||||
|
||||
/**
|
||||
* Find all test cases (PDF + JSON pairs) in .nogit/
|
||||
*/
|
||||
function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
|
||||
const testDir = path.join(process.cwd(), '.nogit');
|
||||
if (!fs.existsSync(testDir)) {
|
||||
return [];
|
||||
}
|
||||
|
||||
const files = fs.readdirSync(testDir);
|
||||
const pdfFiles = files.filter((f: string) => f.endsWith('.pdf'));
|
||||
const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
|
||||
|
||||
for (const pdf of pdfFiles) {
|
||||
const baseName = pdf.replace('.pdf', '');
|
||||
const jsonFile = `${baseName}.json`;
|
||||
if (files.includes(jsonFile)) {
|
||||
testCases.push({
|
||||
name: baseName,
|
||||
pdfPath: path.join(testDir, pdf),
|
||||
jsonPath: path.join(testDir, jsonFile),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return testCases.sort((a, b) => a.name.localeCompare(b.name));
|
||||
}
|
||||
|
||||
// Tests
|
||||
|
||||
tap.test('setup: ensure containers are running', async () => {
|
||||
console.log('\n[Setup] Checking Docker containers...\n');
|
||||
|
||||
// Nanonets for OCR
|
||||
const nanonetsOk = await ensureNanonetsOcr();
|
||||
expect(nanonetsOk).toBeTrue();
|
||||
|
||||
// Ollama for Qwen3
|
||||
const ollamaOk = await ensureMiniCpm();
|
||||
expect(ollamaOk).toBeTrue();
|
||||
|
||||
// Qwen3 model
|
||||
const qwenOk = await ensureQwen3();
|
||||
expect(qwenOk).toBeTrue();
|
||||
|
||||
console.log('\n[Setup] All containers ready!\n');
|
||||
});
|
||||
|
||||
tap.test('should have models available', async () => {
|
||||
// Check Nanonets
|
||||
const nanonetsResp = await fetch(`${NANONETS_URL}/models`);
|
||||
expect(nanonetsResp.ok).toBeTrue();
|
||||
|
||||
// Check Qwen3
|
||||
const ollamaResp = await fetch(`${OLLAMA_URL}/api/tags`);
|
||||
expect(ollamaResp.ok).toBeTrue();
|
||||
const data = await ollamaResp.json();
|
||||
const modelNames = data.models.map((m: { name: string }) => m.name);
|
||||
expect(modelNames.some((name: string) => name.includes('qwen3'))).toBeTrue();
|
||||
});
|
||||
|
||||
const testCases = findTestCases();
|
||||
console.log(`\nFound ${testCases.length} bank statement test cases (Nanonets + Qwen3)\n`);
|
||||
|
||||
let passedCount = 0;
|
||||
let failedCount = 0;
|
||||
|
||||
for (const testCase of testCases) {
|
||||
tap.test(`should extract: ${testCase.name}`, async () => {
|
||||
const expected: ITransaction[] = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
|
||||
console.log(`\n=== ${testCase.name} ===`);
|
||||
console.log(`Expected: ${expected.length} transactions`);
|
||||
|
||||
const images = convertPdfToImages(testCase.pdfPath);
|
||||
console.log(` Pages: ${images.length}`);
|
||||
|
||||
const extracted = await extractTransactions(images);
|
||||
console.log(` Extracted: ${extracted.length} transactions`);
|
||||
|
||||
const result = compareTransactions(extracted, expected);
|
||||
const perfectMatch = result.matches === result.total && extracted.length === expected.length;
|
||||
|
||||
if (perfectMatch) {
|
||||
passedCount++;
|
||||
console.log(` Result: PASS (${result.matches}/${result.total})`);
|
||||
} else {
|
||||
failedCount++;
|
||||
console.log(` Result: FAIL (${result.matches}/${result.total})`);
|
||||
result.errors.slice(0, 10).forEach((e) => console.log(` - ${e}`));
|
||||
}
|
||||
|
||||
if (result.variations.length > 0) {
|
||||
console.log(` Counterparty variations (${result.variations.length}):`);
|
||||
result.variations.slice(0, 5).forEach((v) => console.log(` ${v}`));
|
||||
}
|
||||
|
||||
expect(result.matches).toEqual(result.total);
|
||||
expect(extracted.length).toEqual(expected.length);
|
||||
});
|
||||
}
|
||||
|
||||
tap.test('summary', async () => {
|
||||
const total = testCases.length;
|
||||
console.log(`\n======================================================`);
|
||||
console.log(` Bank Statement Summary (Nanonets + Qwen3 Pipeline)`);
|
||||
console.log(`======================================================`);
|
||||
console.log(` Stage 1: Nanonets-OCR-s (document -> markdown)`);
|
||||
console.log(` Stage 2: Qwen3 8B (markdown -> JSON)`);
|
||||
console.log(` Passed: ${passedCount}/${total}`);
|
||||
console.log(` Failed: ${failedCount}/${total}`);
|
||||
console.log(`======================================================\n`);
|
||||
});
|
||||
|
||||
export default tap.start();
|
||||
584
test/test.invoices.nanonets.ts
Normal file
584
test/test.invoices.nanonets.ts
Normal file
@@ -0,0 +1,584 @@
|
||||
/**
|
||||
* Invoice extraction using Nanonets-OCR-s + Qwen3 (two-stage pipeline)
|
||||
*
|
||||
* Stage 1: Nanonets-OCR-s converts document pages to markdown (its strength)
|
||||
* Stage 2: Qwen3 extracts structured JSON from the combined markdown
|
||||
*
|
||||
* This leverages each model's strengths:
|
||||
* - Nanonets: Document OCR with semantic tags
|
||||
* - Qwen3: Text understanding and JSON extraction
|
||||
*/
|
||||
import { tap, expect } from '@git.zone/tstest/tapbundle';
|
||||
import * as fs from 'fs';
|
||||
import * as path from 'path';
|
||||
import { execSync } from 'child_process';
|
||||
import * as os from 'os';
|
||||
import { ensureNanonetsOcr, ensureMiniCpm } from './helpers/docker.js';
|
||||
|
||||
const NANONETS_URL = 'http://localhost:8000/v1';
|
||||
const NANONETS_MODEL = 'nanonets/Nanonets-OCR-s';
|
||||
|
||||
const OLLAMA_URL = 'http://localhost:11434';
|
||||
const QWEN_MODEL = 'qwen3:8b';
|
||||
|
||||
interface IInvoice {
|
||||
invoice_number: string;
|
||||
invoice_date: string;
|
||||
vendor_name: string;
|
||||
currency: string;
|
||||
net_amount: number;
|
||||
vat_amount: number;
|
||||
total_amount: number;
|
||||
}
|
||||
|
||||
// Nanonets-specific prompt for document OCR to markdown
|
||||
const NANONETS_OCR_PROMPT = `Extract the text from the above document as if you were reading it naturally.
|
||||
Return the tables in html format.
|
||||
Return the equations in LaTeX representation.
|
||||
If there is an image in the document and image caption is not present, add a small description inside <img></img> tag.
|
||||
Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>.
|
||||
Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number>.`;
|
||||
|
||||
// JSON extraction prompt for Qwen3
|
||||
const JSON_EXTRACTION_PROMPT = `You are an invoice data extractor. Below is an invoice document converted to text/markdown. Extract the key invoice fields as JSON.
|
||||
|
||||
IMPORTANT RULES:
|
||||
1. invoice_number: The unique invoice/document number (NOT VAT ID, NOT customer ID)
|
||||
2. invoice_date: Format as YYYY-MM-DD
|
||||
3. vendor_name: The company that issued the invoice
|
||||
4. currency: EUR, USD, or GBP
|
||||
5. net_amount: Amount before tax
|
||||
6. vat_amount: Tax/VAT amount
|
||||
7. total_amount: Final total (gross amount)
|
||||
|
||||
Return ONLY this JSON format, no explanation:
|
||||
{
|
||||
"invoice_number": "INV-2024-001",
|
||||
"invoice_date": "2024-01-15",
|
||||
"vendor_name": "Company Name",
|
||||
"currency": "EUR",
|
||||
"net_amount": 100.00,
|
||||
"vat_amount": 19.00,
|
||||
"total_amount": 119.00
|
||||
}
|
||||
|
||||
INVOICE TEXT:
|
||||
`;
|
||||
|
||||
/**
|
||||
* Convert PDF to PNG images using ImageMagick
|
||||
*/
|
||||
function convertPdfToImages(pdfPath: string): string[] {
|
||||
const tempDir = fs.mkdtempSync(path.join(os.tmpdir(), 'pdf-convert-'));
|
||||
const outputPattern = path.join(tempDir, 'page-%d.png');
|
||||
|
||||
try {
|
||||
// Use 150 DPI to keep images within model's context length
|
||||
execSync(
|
||||
`convert -density 150 -quality 90 "${pdfPath}" -background white -alpha remove "${outputPattern}"`,
|
||||
{ stdio: 'pipe' }
|
||||
);
|
||||
|
||||
const files = fs.readdirSync(tempDir).filter((f) => f.endsWith('.png')).sort();
|
||||
const images: string[] = [];
|
||||
|
||||
for (const file of files) {
|
||||
const imagePath = path.join(tempDir, file);
|
||||
const imageData = fs.readFileSync(imagePath);
|
||||
images.push(imageData.toString('base64'));
|
||||
}
|
||||
|
||||
return images;
|
||||
} finally {
|
||||
fs.rmSync(tempDir, { recursive: true, force: true });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 1: Convert a single page to markdown using Nanonets-OCR-s
|
||||
*/
|
||||
async function convertPageToMarkdown(image: string, pageNum: number): Promise<string> {
|
||||
console.log(` [Nanonets] Converting page ${pageNum} to markdown...`);
|
||||
const startTime = Date.now();
|
||||
|
||||
const response = await fetch(`${NANONETS_URL}/chat/completions`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': 'Bearer dummy',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: NANONETS_MODEL,
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content: [
|
||||
{ type: 'image_url', image_url: { url: `data:image/png;base64,${image}` }},
|
||||
{ type: 'text', text: NANONETS_OCR_PROMPT },
|
||||
],
|
||||
}],
|
||||
max_tokens: 4096,
|
||||
temperature: 0.0,
|
||||
}),
|
||||
});
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.log(` [Nanonets] ERROR page ${pageNum}: ${response.status} - ${errorText}`);
|
||||
throw new Error(`Nanonets API error: ${response.status}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const content = (data.choices?.[0]?.message?.content || '').trim();
|
||||
console.log(` [Nanonets] Page ${pageNum} converted (${elapsed}s, ${content.length} chars)`);
|
||||
return content;
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 1: Convert all pages to markdown using Nanonets-OCR-s
|
||||
*/
|
||||
async function convertDocumentToMarkdown(images: string[]): Promise<string> {
|
||||
console.log(` [Stage 1] Converting ${images.length} page(s) to markdown with Nanonets-OCR-s...`);
|
||||
|
||||
const markdownPages: string[] = [];
|
||||
|
||||
for (let i = 0; i < images.length; i++) {
|
||||
const markdown = await convertPageToMarkdown(images[i], i + 1);
|
||||
markdownPages.push(`--- PAGE ${i + 1} ---\n${markdown}`);
|
||||
}
|
||||
|
||||
const fullMarkdown = markdownPages.join('\n\n');
|
||||
console.log(` [Stage 1] Complete: ${fullMarkdown.length} chars total`);
|
||||
return fullMarkdown;
|
||||
}
|
||||
|
||||
/**
|
||||
* Ensure Qwen3 model is available
|
||||
*/
|
||||
async function ensureQwen3(): Promise<boolean> {
|
||||
try {
|
||||
const response = await fetch(`${OLLAMA_URL}/api/tags`);
|
||||
if (response.ok) {
|
||||
const data = await response.json();
|
||||
const models = data.models || [];
|
||||
if (models.some((m: { name: string }) => m.name === QWEN_MODEL)) {
|
||||
console.log(` [Ollama] Model available: ${QWEN_MODEL}`);
|
||||
return true;
|
||||
}
|
||||
}
|
||||
} catch {
|
||||
return false;
|
||||
}
|
||||
|
||||
console.log(` [Ollama] Pulling ${QWEN_MODEL}...`);
|
||||
const pullResponse = await fetch(`${OLLAMA_URL}/api/pull`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({ name: QWEN_MODEL, stream: false }),
|
||||
});
|
||||
|
||||
return pullResponse.ok;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse amount from string (handles European format)
|
||||
*/
|
||||
function parseAmount(s: string | number | undefined): number {
|
||||
if (s === undefined || s === null) return 0;
|
||||
if (typeof s === 'number') return s;
|
||||
const match = s.match(/([\d.,]+)/);
|
||||
if (!match) return 0;
|
||||
const numStr = match[1];
|
||||
// Handle European format: 1.234,56 -> 1234.56
|
||||
const normalized = numStr.includes(',') && numStr.indexOf(',') > numStr.lastIndexOf('.')
|
||||
? numStr.replace(/\./g, '').replace(',', '.')
|
||||
: numStr.replace(/,/g, '');
|
||||
return parseFloat(normalized) || 0;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract invoice number from potentially verbose response
|
||||
*/
|
||||
function extractInvoiceNumber(s: string | undefined): string {
|
||||
if (!s) return '';
|
||||
let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
|
||||
const patterns = [
|
||||
/\b([A-Z]{2,3}\d{10,})\b/i, // IEE2022006460244
|
||||
/\b([A-Z]\d{8,})\b/i, // R0014359508
|
||||
/\b(INV[-\s]?\d{4}[-\s]?\d+)\b/i, // INV-2024-001
|
||||
/\b(\d{7,})\b/, // 1579087430
|
||||
];
|
||||
for (const pattern of patterns) {
|
||||
const match = clean.match(pattern);
|
||||
if (match) return match[1];
|
||||
}
|
||||
return clean.replace(/[^A-Z0-9-]/gi, '').trim() || clean;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract date (YYYY-MM-DD) from response
|
||||
*/
|
||||
function extractDate(s: string | undefined): string {
|
||||
if (!s) return '';
|
||||
let clean = s.replace(/\*\*/g, '').replace(/`/g, '').trim();
|
||||
const isoMatch = clean.match(/(\d{4}-\d{2}-\d{2})/);
|
||||
if (isoMatch) return isoMatch[1];
|
||||
// Try DD/MM/YYYY or DD.MM.YYYY
|
||||
const dmyMatch = clean.match(/(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})/);
|
||||
if (dmyMatch) {
|
||||
return `${dmyMatch[3]}-${dmyMatch[2].padStart(2, '0')}-${dmyMatch[1].padStart(2, '0')}`;
|
||||
}
|
||||
return clean.replace(/[^\d-]/g, '').trim();
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract currency
|
||||
*/
|
||||
function extractCurrency(s: string | undefined): string {
|
||||
if (!s) return 'EUR';
|
||||
const upper = s.toUpperCase();
|
||||
if (upper.includes('EUR') || upper.includes('€')) return 'EUR';
|
||||
if (upper.includes('USD') || upper.includes('$')) return 'USD';
|
||||
if (upper.includes('GBP') || upper.includes('£')) return 'GBP';
|
||||
return 'EUR';
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract JSON from response (handles markdown code blocks)
|
||||
*/
|
||||
function extractJsonFromResponse(response: string): Record<string, unknown> | null {
|
||||
// Remove thinking tags if present (Qwen3 may include <think>...</think>)
|
||||
let cleanResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();
|
||||
|
||||
// Try to find JSON in markdown code block
|
||||
const codeBlockMatch = cleanResponse.match(/```(?:json)?\s*([\s\S]*?)```/);
|
||||
const jsonStr = codeBlockMatch ? codeBlockMatch[1].trim() : cleanResponse;
|
||||
|
||||
try {
|
||||
return JSON.parse(jsonStr);
|
||||
} catch {
|
||||
// Try to find JSON object pattern
|
||||
const jsonMatch = jsonStr.match(/\{[\s\S]*\}/);
|
||||
if (jsonMatch) {
|
||||
try {
|
||||
return JSON.parse(jsonMatch[0]);
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse JSON response into IInvoice
|
||||
*/
|
||||
function parseJsonToInvoice(response: string): IInvoice | null {
|
||||
const parsed = extractJsonFromResponse(response);
|
||||
if (!parsed) return null;
|
||||
|
||||
return {
|
||||
invoice_number: extractInvoiceNumber(String(parsed.invoice_number || '')),
|
||||
invoice_date: extractDate(String(parsed.invoice_date || '')),
|
||||
vendor_name: String(parsed.vendor_name || '').replace(/\*\*/g, '').replace(/`/g, '').trim(),
|
||||
currency: extractCurrency(String(parsed.currency || '')),
|
||||
net_amount: parseAmount(parsed.net_amount as string | number),
|
||||
vat_amount: parseAmount(parsed.vat_amount as string | number),
|
||||
total_amount: parseAmount(parsed.total_amount as string | number),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 2: Extract invoice from markdown using Qwen3
|
||||
*/
|
||||
async function extractInvoiceFromMarkdown(markdown: string, queryId: string): Promise<IInvoice | null> {
|
||||
console.log(` [${queryId}] Sending markdown to ${QWEN_MODEL}...`);
|
||||
const startTime = Date.now();
|
||||
|
||||
const response = await fetch(`${OLLAMA_URL}/api/chat`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: QWEN_MODEL,
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content: JSON_EXTRACTION_PROMPT + markdown,
|
||||
}],
|
||||
stream: false,
|
||||
options: {
|
||||
num_predict: 2000,
|
||||
temperature: 0.1,
|
||||
},
|
||||
}),
|
||||
});
|
||||
|
||||
const elapsed = ((Date.now() - startTime) / 1000).toFixed(1);
|
||||
|
||||
if (!response.ok) {
|
||||
console.log(` [${queryId}] ERROR: ${response.status} (${elapsed}s)`);
|
||||
throw new Error(`Ollama API error: ${response.status}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
const content = (data.message?.content || '').trim();
|
||||
console.log(` [${queryId}] Response received (${elapsed}s, ${content.length} chars)`);
|
||||
|
||||
return parseJsonToInvoice(content);
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare two invoices for consensus (key fields must match)
|
||||
*/
|
||||
function invoicesMatch(a: IInvoice, b: IInvoice): boolean {
|
||||
const numMatch = a.invoice_number.toLowerCase() === b.invoice_number.toLowerCase();
|
||||
const dateMatch = a.invoice_date === b.invoice_date;
|
||||
const totalMatch = Math.abs(a.total_amount - b.total_amount) < 0.02;
|
||||
return numMatch && dateMatch && totalMatch;
|
||||
}
|
||||
|
||||
/**
|
||||
* Stage 2: Extract invoice using Qwen3 with consensus
|
||||
*/
|
||||
async function extractWithConsensus(markdown: string): Promise<IInvoice> {
|
||||
const MAX_ATTEMPTS = 3;
|
||||
console.log(` [Stage 2] Extracting invoice with ${QWEN_MODEL} (consensus)...`);
|
||||
|
||||
for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) {
|
||||
console.log(`\n [Stage 2] --- Attempt ${attempt}/${MAX_ATTEMPTS} ---`);
|
||||
|
||||
// Extract twice
|
||||
const inv1 = await extractInvoiceFromMarkdown(markdown, `A${attempt}Q1`);
|
||||
const inv2 = await extractInvoiceFromMarkdown(markdown, `A${attempt}Q2`);
|
||||
|
||||
if (!inv1 || !inv2) {
|
||||
console.log(` [Stage 2] Parsing failed, retrying...`);
|
||||
continue;
|
||||
}
|
||||
|
||||
console.log(` [Stage 2] Q1: ${inv1.invoice_number} | ${inv1.invoice_date} | ${inv1.total_amount} ${inv1.currency}`);
|
||||
console.log(` [Stage 2] Q2: ${inv2.invoice_number} | ${inv2.invoice_date} | ${inv2.total_amount} ${inv2.currency}`);
|
||||
|
||||
if (invoicesMatch(inv1, inv2)) {
|
||||
console.log(` [Stage 2] CONSENSUS REACHED`);
|
||||
return inv2;
|
||||
}
|
||||
|
||||
console.log(` [Stage 2] NO CONSENSUS`);
|
||||
}
|
||||
|
||||
// Fallback: use last response
|
||||
console.log(`\n [Stage 2] === FALLBACK ===`);
|
||||
const fallback = await extractInvoiceFromMarkdown(markdown, 'FALLBACK');
|
||||
|
||||
if (fallback) {
|
||||
console.log(` [Stage 2] ~ FALLBACK: ${fallback.invoice_number} | ${fallback.invoice_date} | ${fallback.total_amount}`);
|
||||
return fallback;
|
||||
}
|
||||
|
||||
// Return empty invoice if all else fails
|
||||
return {
|
||||
invoice_number: '',
|
||||
invoice_date: '',
|
||||
vendor_name: '',
|
||||
currency: 'EUR',
|
||||
net_amount: 0,
|
||||
vat_amount: 0,
|
||||
total_amount: 0,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Full pipeline: PDF -> Images -> Markdown -> JSON
|
||||
*/
|
||||
async function extractInvoice(images: string[]): Promise<IInvoice> {
|
||||
// Stage 1: Convert to markdown
|
||||
const markdown = await convertDocumentToMarkdown(images);
|
||||
|
||||
// Stage 2: Extract invoice with consensus
|
||||
const invoice = await extractWithConsensus(markdown);
|
||||
|
||||
return invoice;
|
||||
}
|
||||
|
||||
/**
|
||||
* Normalize date to YYYY-MM-DD
|
||||
*/
|
||||
function normalizeDate(dateStr: string | null): string {
|
||||
if (!dateStr) return '';
|
||||
if (/^\d{4}-\d{2}-\d{2}$/.test(dateStr)) return dateStr;
|
||||
|
||||
const monthMap: Record<string, string> = {
|
||||
JAN: '01', FEB: '02', MAR: '03', APR: '04', MAY: '05', JUN: '06',
|
||||
JUL: '07', AUG: '08', SEP: '09', OCT: '10', NOV: '11', DEC: '12',
|
||||
};
|
||||
|
||||
let match = dateStr.match(/^(\d{1,2})-([A-Z]{3})-(\d{4})$/i);
|
||||
if (match) {
|
||||
return `${match[3]}-${monthMap[match[2].toUpperCase()] || '01'}-${match[1].padStart(2, '0')}`;
|
||||
}
|
||||
|
||||
match = dateStr.match(/^(\d{1,2})[\/.](\d{1,2})[\/.](\d{4})$/);
|
||||
if (match) {
|
||||
return `${match[3]}-${match[2].padStart(2, '0')}-${match[1].padStart(2, '0')}`;
|
||||
}
|
||||
|
||||
return dateStr;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare extracted invoice against expected
|
||||
*/
|
||||
function compareInvoice(
|
||||
extracted: IInvoice,
|
||||
expected: IInvoice
|
||||
): { match: boolean; errors: string[] } {
|
||||
const errors: string[] = [];
|
||||
|
||||
// Compare invoice number (normalize by removing spaces and case)
|
||||
const extNum = extracted.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
|
||||
const expNum = expected.invoice_number?.replace(/\s/g, '').toLowerCase() || '';
|
||||
if (extNum !== expNum) {
|
||||
errors.push(`invoice_number: expected "${expected.invoice_number}", got "${extracted.invoice_number}"`);
|
||||
}
|
||||
|
||||
// Compare date
|
||||
if (normalizeDate(extracted.invoice_date) !== normalizeDate(expected.invoice_date)) {
|
||||
errors.push(`invoice_date: expected "${expected.invoice_date}", got "${extracted.invoice_date}"`);
|
||||
}
|
||||
|
||||
// Compare total amount (with tolerance)
|
||||
if (Math.abs(extracted.total_amount - expected.total_amount) > 0.02) {
|
||||
errors.push(`total_amount: expected ${expected.total_amount}, got ${extracted.total_amount}`);
|
||||
}
|
||||
|
||||
// Compare currency
|
||||
if (extracted.currency?.toUpperCase() !== expected.currency?.toUpperCase()) {
|
||||
errors.push(`currency: expected "${expected.currency}", got "${extracted.currency}"`);
|
||||
}
|
||||
|
||||
return { match: errors.length === 0, errors };
|
||||
}
|
||||
|
||||
/**
|
||||
* Find all test cases (PDF + JSON pairs) in .nogit/invoices/
|
||||
*/
|
||||
function findTestCases(): Array<{ name: string; pdfPath: string; jsonPath: string }> {
|
||||
const testDir = path.join(process.cwd(), '.nogit/invoices');
|
||||
if (!fs.existsSync(testDir)) {
|
||||
return [];
|
||||
}
|
||||
|
||||
const files = fs.readdirSync(testDir);
|
||||
const pdfFiles = files.filter((f) => f.endsWith('.pdf'));
|
||||
const testCases: Array<{ name: string; pdfPath: string; jsonPath: string }> = [];
|
||||
|
||||
for (const pdf of pdfFiles) {
|
||||
const baseName = pdf.replace('.pdf', '');
|
||||
const jsonFile = `${baseName}.json`;
|
||||
if (files.includes(jsonFile)) {
|
||||
testCases.push({
|
||||
name: baseName,
|
||||
pdfPath: path.join(testDir, pdf),
|
||||
jsonPath: path.join(testDir, jsonFile),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
testCases.sort((a, b) => a.name.localeCompare(b.name));
|
||||
return testCases;
|
||||
}
|
||||
|
||||
// Tests
|
||||
|
||||
tap.test('setup: ensure containers are running', async () => {
|
||||
console.log('\n[Setup] Checking Docker containers...\n');
|
||||
|
||||
// Nanonets for OCR
|
||||
const nanonetsOk = await ensureNanonetsOcr();
|
||||
expect(nanonetsOk).toBeTrue();
|
||||
|
||||
// Ollama for Qwen3
|
||||
const ollamaOk = await ensureMiniCpm();
|
||||
expect(ollamaOk).toBeTrue();
|
||||
|
||||
// Qwen3 model
|
||||
const qwenOk = await ensureQwen3();
|
||||
expect(qwenOk).toBeTrue();
|
||||
|
||||
console.log('\n[Setup] All containers ready!\n');
|
||||
});
|
||||
|
||||
tap.test('should have models available', async () => {
|
||||
// Check Nanonets
|
||||
const nanonetsResp = await fetch(`${NANONETS_URL}/models`);
|
||||
expect(nanonetsResp.ok).toBeTrue();
|
||||
|
||||
// Check Qwen3
|
||||
const ollamaResp = await fetch(`${OLLAMA_URL}/api/tags`);
|
||||
expect(ollamaResp.ok).toBeTrue();
|
||||
const data = await ollamaResp.json();
|
||||
const modelNames = data.models.map((m: { name: string }) => m.name);
|
||||
expect(modelNames.some((name: string) => name.includes('qwen3'))).toBeTrue();
|
||||
});
|
||||
|
||||
const testCases = findTestCases();
|
||||
console.log(`\nFound ${testCases.length} invoice test cases (Nanonets + Qwen3)\n`);
|
||||
|
||||
let passedCount = 0;
|
||||
let failedCount = 0;
|
||||
const processingTimes: number[] = [];
|
||||
|
||||
for (const testCase of testCases) {
|
||||
tap.test(`should extract invoice: ${testCase.name}`, async () => {
|
||||
const expected: IInvoice = JSON.parse(fs.readFileSync(testCase.jsonPath, 'utf-8'));
|
||||
console.log(`\n=== ${testCase.name} ===`);
|
||||
console.log(`Expected: ${expected.invoice_number} | ${expected.invoice_date} | ${expected.total_amount} ${expected.currency}`);
|
||||
|
||||
const startTime = Date.now();
|
||||
const images = convertPdfToImages(testCase.pdfPath);
|
||||
console.log(` Pages: ${images.length}`);
|
||||
|
||||
const extracted = await extractInvoice(images);
|
||||
console.log(` Extracted: ${extracted.invoice_number} | ${extracted.invoice_date} | ${extracted.total_amount} ${extracted.currency}`);
|
||||
|
||||
const elapsedMs = Date.now() - startTime;
|
||||
processingTimes.push(elapsedMs);
|
||||
|
||||
const result = compareInvoice(extracted, expected);
|
||||
|
||||
if (result.match) {
|
||||
passedCount++;
|
||||
console.log(` Result: MATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
} else {
|
||||
failedCount++;
|
||||
console.log(` Result: MISMATCH (${(elapsedMs / 1000).toFixed(1)}s)`);
|
||||
result.errors.forEach((e) => console.log(` - ${e}`));
|
||||
}
|
||||
|
||||
expect(result.match).toBeTrue();
|
||||
});
|
||||
}
|
||||
|
||||
tap.test('summary', async () => {
|
||||
const totalInvoices = testCases.length;
|
||||
const accuracy = totalInvoices > 0 ? (passedCount / totalInvoices) * 100 : 0;
|
||||
const totalTimeMs = processingTimes.reduce((a, b) => a + b, 0);
|
||||
const avgTimeSec = processingTimes.length > 0 ? totalTimeMs / processingTimes.length / 1000 : 0;
|
||||
|
||||
console.log(`\n========================================`);
|
||||
console.log(` Invoice Extraction Summary`);
|
||||
console.log(` (Nanonets + Qwen3 Pipeline)`);
|
||||
console.log(`========================================`);
|
||||
console.log(` Stage 1: Nanonets-OCR-s (doc -> md)`);
|
||||
console.log(` Stage 2: Qwen3 8B (md -> JSON)`);
|
||||
console.log(` Passed: ${passedCount}/${totalInvoices}`);
|
||||
console.log(` Failed: ${failedCount}/${totalInvoices}`);
|
||||
console.log(` Accuracy: ${accuracy.toFixed(1)}%`);
|
||||
console.log(`----------------------------------------`);
|
||||
console.log(` Total time: ${(totalTimeMs / 1000).toFixed(1)}s`);
|
||||
console.log(` Avg per inv: ${avgTimeSec.toFixed(1)}s`);
|
||||
console.log(`========================================\n`);
|
||||
});
|
||||
|
||||
export default tap.start();
|
||||
Reference in New Issue
Block a user